Accounting for Unresolved Clouds in a 1D Infrared Radiative Transfer Model. Part I: Solution for Radiative Transfer, Including Cloud Scattering and Overlap

Li, J.

doi:10.1175/1520-0469(2002)059<3302:afucia>2.0.co;2

Cited by 66 publications

(47 citation statements)

References 35 publications

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“…The latter has the ability to use either the deterministic solvers described in Li (2002) ;Li, Dobbie, Räisänen, and Min (2005); Li and Barker (2005) or the Monte Carlo Independent Column Approximation (McICA; Barker et al, 2008;Pincus, Barker, and Morcrette, 2003). Although both schemes share the ability to account for radiative transfer through overlapping and horizontally inhomogeneous clouds, only the implementation of McICA will be described here.…”

Section: Radiative Transfermentioning

confidence: 99%

“…This requires, in addition to the optical properties described in the previous section, a radiative transfer solver and a method to model the unresolved structure, namely clouds. The radiative transfer solvers, one for solar and one for the infrared, are effectively those described in and Li (2002) although they have been simplified to solve the radiative transfer only through overcast homogeneous clouds in multiple layers because this is all that is required for McICA. The unresolved cloud structure is provided by a stochastic cloud generator (Räisänen, Barker, Khairoutdinov, Li, and Randall, 2004).…”

Section: Radiative Transfermentioning

confidence: 99%

“…The effect of atmospheric spherical curvature and refraction on the effective pathlength is accounted for by adjusting the solar zenith angle using 1 μ e = 24.35 2μ 0 + 498.5225μ 2 0 + 1 , where μ 0 is the cosine of solar zenith angle and μ e is the cosine of effective solar zenith angle (Li and Shibata, 2006). For infrared radiation, a two-stream solver is also used along with a methodology to account for the scattering by cloud and aerosol particles efficiently (Li, 2002). In the CKD model, wavenumber 2500 cm −1 delineates the solar and infrared regions for the application of each of the radiative transfer solvers.…”

Section: Radiative Transfermentioning

confidence: 99%

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The Canadian Fourth Generation Atmospheric Global Climate Model (CanAM4). Part I: Representation of Physical Processes

et al. 2013

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The Canadian Centre for Climate Modelling and Analysis (CCCma) has developed the fourth generation of the Canadian Atmospheric Global Climate Model (CanAM4). The new model includes substantially modified physical parameterizations compared to its predecessor. In particular, the treatment of clouds, cloud radiative effects, and precipitation has been modified. Aerosol direct and indirect effects are calculated based on a bulk aerosol scheme. Simulation results for present-day global climate are analyzed, with a focus on cloud radiative effects and precipitation. Good overall agreement is found between climatological mean short-and longwave cloud radiative effects and observations from the Clouds and Earth's Radiant Energy System (CERES) experiment. An analysis of the responses of cloud radiative effects to variations in climate will be presented in a companion paper.RÉSUMÉ [Traduit par la rédaction] Le Centre canadien de la modélisation et de l'analyse climatique (CCmaC) a mis au point la quatrième génération du modèle canadien de circulation générale de l'atmosphère (CanAM4). Le nouveau modèle comprend des paramétrisations physiques passablement modifiées comparativement à son prédé-cesseur. En particulier, le traitement des nuages, des effets radiatifs des nuages et des précipitations a été modifié. Les effets directs et indirects des aérosols sont calculés à l'aide d'un schéma d'aérosols en bloc. Nous analysons des résultats de simulation pour le climat général du jour présent en mettant l'accent sur les effets radiatifs des nuages et les précipitations. Nous trouvons un bon accord général entre la moyenne climatologique des effets radiatifs des nuages pour les courtes et les grandes longueurs d'onde et les observations de l'expérience CERES (Clouds and Earth's Radiant Energy System). Une analyse de la réponse des effets radiatifs des nuages aux variations du climat sera présentée dans un article connexe.

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Section: Radiative Transfermentioning

confidence: 99%

Section: Radiative Transfermentioning

confidence: 99%

Section: Radiative Transfermentioning

confidence: 99%

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The Canadian Fourth Generation Atmospheric Global Climate Model (CanAM4). Part I: Representation of Physical Processes

et al. 2013

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“…SW fluxes were computed by a twostream approximation, while for the LW an emissivity-type approach with corrections for scattering was used (Li, 2002). Gaseous transmittances (H 2 O, CO 2 and O 3 for SW; H 2 O, CO 2 , O 3 , CH 4 , N 2 O, CFCl 3 and CF 2 Cl 2 for LW) were computed using the correlated k-distribution method with 31 quadrature points in cumulative probability space for SW and 46 for LW (Scinocca et al, 2008).…”

Section: D Radiative Transfer For Constructed Domainsmentioning

confidence: 99%

A 3D cloud‐construction algorithm for the EarthCARE satellite mission

Barker

Jerg

Wehr

et al. 2011

Quart J Royal Meteoro Soc

144

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† The contribution of H. W. Barker to this article was prepared as part of his official duties as a Canadian government employee This article presents and assesses an algorithm that constructs 3D distributions of cloud from passive satellite imagery and collocated 2D nadir profiles of cloud properties inferred synergistically from lidar, cloud radar and imager data. It effectively widens the active-passive retrieved cross-section (RXS) of cloud properties, thereby enabling computation of radiative fluxes and radiances that can be compared with measured values in an attempt to perform radiative closure experiments that aim to assess the RXS. For this introductory study, A-train data were used to verify the scene-construction algorithm and only 1D radiative transfer calculations were performed.The construction algorithm fills off-RXS recipient pixels by computing sums of squared differences (a cost function F) between their spectral radiances and those of potential donor pixels/columns on the RXS. Of the RXS pixels with F lower than a certain value, the one with the smallest Euclidean distance to the recipient pixel is designated as the donor, and its retrieved cloud properties and other attributes such as 1D radiative heating rates are consigned to the recipient. It is shown that both the RXS itself and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery can be reconstructed extremely well using just visible and thermal infrared channels. Suitable donors usually lie within 10 km of the recipient. RXSs and their associated radiative heating profiles are reconstructed best for extensive planar clouds and less reliably for broken convective clouds. Domain-average 1D broadband radiative fluxes at the top of the atmosphere (TOA) for (21 km) 2 domains constructed from MODIS, CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data agree well with coincidental values derived from Clouds and the Earth's Radiant Energy System (CERES) radiances: differences between modelled and measured reflected shortwave fluxes are within ±10 W m −2 for ∼35% of the several hundred domains constructed for eight orbits. Correspondingly, for outgoing longwave radiation ∼65% are within ±10 W m −2 .

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“…The system incorporates seven major complete cloud-aerosol-radiation packages from the latest global weather forecast and climate prediction models used in the key operational centers and research institutions worldwide. These include cam (NCAR) , rrtmg (NCEP, ECMWF, future NCAR) (Clough et al, 2005;Iacono et al, 2008;Morcrette et al, 2008), gfdl (NOAA) (Freidenreich and Ramaswamy, 1999;Schwarzkopf and Ramaswamy, 1999;Clough et al, 1992), gsfc (NASA) (Chou and Suarez, 1999;Chou et al, 2001), cccma (Canada) (Li, 2002;Li and Barker, 2005), cawcr (Australia, also future UKMO) (Sun and Rikus, 1999;Sun, 2008), and flg (popular for DOE/ARM) (Fu and Liou, 1992;Fu et al, 1998; Liou et 8338 X.-Z. Liang and F. Zhang: The cloud-aerosol-radiation (CAR) ensemble modeling system Fig.…”

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confidence: 99%

The cloud–aerosol–radiation (CAR) ensemble modeling system

Liang

Zhang

2013

Atmos. Chem. Phys.

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A cloud–aerosol–radiation (CAR) ensemble modeling system has been developed to incorporate the largest choices of alternate parameterizations for cloud properties (cover, water, radius, optics, geometry), aerosol properties (type, profile, optics), radiation transfers (solar, infrared), and their interactions. These schemes form the most comprehensive collection currently available in the literature, including those used by the world's leading general circulation models (GCMs). CAR provides a unique framework to determine (via intercomparison across all schemes), reduce (via optimized ensemble simulations), and attribute specific key factors for (via physical process sensitivity analyses) the model discrepancies and uncertainties in representing greenhouse gas, aerosol, and cloud radiative forcing effects. This study presents a general description of the CAR system and illustrates its capabilities for climate modeling applications, especially in the context of estimating climate sensitivity and uncertainty range caused by cloud–aerosol–radiation interactions. For demonstration purposes, the evaluation is based on several CAR standalone and coupled climate model experiments, each comparing a limited subset of the full system ensemble with up to 896 members. It is shown that the quantification of radiative forcings and climate impacts strongly depends on the choices of the cloud, aerosol, and radiation schemes. The prevailing schemes used in current GCMs are likely insufficient in variety and physically biased in a significant way. There exists large room for improvement by optimally combining radiation transfer with cloud property schemes

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Accounting for Unresolved Clouds in a 1D Infrared Radiative Transfer Model. Part I: Solution for Radiative Transfer, Including Cloud Scattering and Overlap

Cited by 66 publications

References 35 publications

The Canadian Fourth Generation Atmospheric Global Climate Model (CanAM4). Part I: Representation of Physical Processes

The Canadian Fourth Generation Atmospheric Global Climate Model (CanAM4). Part I: Representation of Physical Processes

A 3D cloud‐construction algorithm for the EarthCARE satellite mission

The cloud–aerosol–radiation (CAR) ensemble modeling system

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